sign language.py

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(26, activation='softmax')
])

# Compile Model. 
model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

# Train the Model
history = model.fit_generator(train_datagen.flow(training_images, training_labels, batch_size=32),
                              steps_per_epoch=len(training_images) / 32,
                              epochs=10,
                              validation_data=validation_datagen.flow(testing_images, testing_labels, batch_size=32),
                              validation_steps=len(testing_images) / 32)



model.evaluate(testing_images, testing_labels, verbose=0)